Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation

Abstract

Neuroimaging is an indispensable tool in neuroscience and medical research, enabling precise investigations into brain structure and function (Yen, Lin, and Chiang 2023;Yan et al. 2022;Shoeibi et al. 2023;Botvinik-Nezer and Wager 2023;Leite et al. 2024;Wager and Smith 2003). Techniques such as Magnetic Resonance Imaging (MRI) generate vast amounts of sensitive data, rich in insights yet fraught with privacy challenges (Saponaro et al. 2022;Cali et al. 2023;Li et al. 2020;Zou et al. 2024;Acar et al. 2023). As scientific progress depends on data sharing and collaboration (Martone 2023), balancing these needs with robust privacy preservation has become a critical concern (Zhang et al. 2020). This special issue addresses this challenge by exploring innovative methodologies, frameworks, and technologies that advance the field while safeguarding individual privacy.The issue aims to promote interdisciplinary research into privacy-preserving solutions for neuroimaging analysis, ensuring compliance with ethical and legal standards (Li et al. 2020). It seeks to balance data utility with privacy protections by fostering methods for anonymization, leveraging AI tools such as federated learning and differential privacy, and aligning technologies with global governance frameworks (Zou et al. 2024;Jeon et al. 2020;Dwork 2006;Abadi et al. 2016). This issue serves as a roadmap for ethical neuroimaging research and a platform for dialogue among neuroscientists, AI researchers, ethicists, and policymakers.This issue features five papers that exemplify the breadth and depth of research at the intersection of privacy, neuroimaging, and artificial intelligence. Each contribution highlights a unique facet of the privacy-preserving landscape, collectively offering a comprehensive exploration of the field's current state and future potential.The first paper 1 tackles the pervasive issue of inflated effect sizes in small-sample neuroimaging studies, a challenge that undermines reproducibility and generalizability. By employing hierarchical Bayesian models, the authors demonstrate how statistical recalibration can improve the reliability of findings while enabling collaborative metaanalyses across studies. This methodological advance sets a foundation for ensuring that shared neuroimaging data is not only secure but also statistically robust.The second paper 2 explores AI-driven segmentation methods for intracranial haemorrhage detection in CT scans. Leveraging self-supervised and weakly-supervised learning, the study addresses the need for label-efficient solutions that minimize reliance on large, annotated datasets. This work showcases how AI innovations can enhance efficiency and maintain privacy, particularly in resource-constrained environments where data annotation is a bottleneck.Federated learning takes centre stage in the third and fourth papers, both of which highlight its potential for decentralized neuroimaging analysis. The third paper 3 introduces a secure federated learning framework for Alzheimer's disease detection, incorporating secure aggregation techniques to protect sensitive data during model training. Similarly, the fourth paper 4 presents Sparse Federated Learning for Neuroimaging (NeuroSFL), which optimizes communication efficiency by focusing on sparse sub-networks. Together, these studies underscore the adaptability and scalability of federated learning as a cornerstone of privacypreserving neuroimaging research.The final paper 5 adopts a broader lens, examining the alignment of AI governance frameworks with neuroinformatics practices. By identifying gaps in existing regulations and proposing strategies for harmonization, the authors provide a roadmap for integrating privacy-preserving technologies within the complex landscape of global governance. This contribution emphasizes the importance of aligning technical advancements with ethical principles, ensuring trust and transparency in neuroimaging research.Artificial intelligence serves as a driving force behind many of the contributions in this issue, offering powerful tools for balancing data sharing and privacy. AI-driven methodologies such as federated learning, differential privacy, and explainable AI not only enable secure data analysis but also enhance transparency and trustworthiness (Yuste 2023;White, Blok, and Calhoun 2022;Yang et al. 2022). These technologies address critical challenges, such as mitigating privacy risks in decentralized environments and ensuring that sensitive neuroimaging data remains private without compromising utility. Federated learning, in particular, emerges as a transformative approach, allowing researchers to train models collaboratively without sharing raw data. The secure aggregation and sparsity-focused innovations presented in this issue demonstrate how federated learning can scale to meet the demands of large, heterogeneous neuroimaging datasets. Complementary technologies such as differential privacy and blockchain also hold promise for further enhancing data security and accountability, though their integration into routine neuroimaging workflows remains a challenge.Despite these advancements, significant challenges persist. Balancing data utility with privacy remains a fundamental tension, as techniques that protect privacy often introduce trade-offs in model performance or scalability (Yuste 2023;Mitrovska et al. 2024). For instance, federated learning frameworks are susceptible to performance degradation in non-IID (non-independent and identically distributed) data settings, a common scenario in neuroimaging. Similarly, the computational demands of privacy-preserving technologies may limit their accessibility to smaller research institutions, exacerbating inequities in the field.Ethical and societal challenges add another layer of complexity (Aboy, Minssen, and Vayena 2024;van Kolfschooten and van Oirschot 2024). Cognitive privacy, informed consent, and equitable access to the benefits of neuroimaging research are ongoing concerns (Bublitz, Molnár-Gábor, and Soekadar 2024). The rapid evolution of AI often outpaces the development of regulatory frameworks, creating misalignments between technological capabilities and ethical oversight (Ratto Trabucco 2023;Ienca and Ignatiadis 2020;Wajnerman Paz 2022;Jwa and Martinez-Martin 2024;Yuste et al. 2017;Genser, Damianos, and Yuste 2024). Addressing these gaps will require sustained dialogue among stakeholders, including neuroscientists, technologists, ethicists, and policymakers (Ligthart et al. 2023;Bublitz, Molnár-Gábor, and Soekadar 2024).This special issue highlights the transformative potential of privacy-preserving technologies in neuroimaging, emphasizing the critical balance between advancing data sharing and maintaining individual privacy. By presenting cutting-edge methodologies, practical frameworks, and real-world applications, the contributions collectively offer a comprehensive roadmap for ethical and innovative neuroimaging research. These works demonstrate that privacy and progress can coexist, fostering collaboration and trust across disciplines.The ubiquity of AI further amplifies the need for dynamic and adaptive regulatory frameworks that evolve alongside technological advancements. As neuroimaging intersects with AI-driven innovation, static and siloed regulations are insufficient to address the complexity of overlapping challenges in data governance and privacy. Instead, flexible approaches that align AI's transformative capabilities with ethical oversight are essential to ensuring responsible progress.This issue is a testament to the potential of interdisciplinary collaboration in tackling complex challenges at the intersection of technology, ethics, and neuroscience. By fostering dialogue and innovation, it lays the groundwork for a future where neuroimaging research thrives in an environment of trust, transparency, and shared progress. We invite readers to engage with these contributions, advancing the conversation and shaping a more ethical and innovative future for neuroimaging and beyond. This special issue would not have been possible without the dedication of the authors, whose innovative research forms its foundation, the reviewers, whose constructive feedback ensured its rigor, and the neuroimaging and AI communities, whose contributions drive progress in privacy preservation and scientific discovery.

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